OpenDataScience that is one of the biggest IT communities in the world unites more than 15k Russian-speaking Data Scientists. Among dozens of ODS projects is organizing for researchers and practitioners the biggest Russian-speaking DS conference: DataFest. OpenDataScience also has its own educational mission that it has presented with the Open Machine Learning Course: MLCOURSE.AI.

What is MLCOURSE.AI?

MLCOURSE.AI is a 10-week open Machine Learning course by OpenDataScience launching on October 1, 2018. The course has been designed to perfectly balance theory and practice; therefore, each topic is followed by an assignment with a deadline in a week. You can also take part in several Kaggle Inclass competitions held during the course and do your own projects as well.

Throughout the course, they maintain a student rating that takes into account the credits scored by the students in assignments and Kaggle competitions. In accordance to the final rating, Top students will be listed on a special Wiki page.

So, what does the course comprise of?

The 10-week course comprises of the following:

Articles On Medium,

Assignments,

Tutorials,

Individual Projects,

Kaggle Inclass Competitions.

What are the Prerequisites for the course- Python, Math, DevOps?

Among prerequisites are knowledge of basic concepts from calculus, linear algebra, probability theory and statistics and Python programming skills.

Moving towards Python, interactive tutorials like CodeAcademy, DataCamp or DataQuest even will suffice.

Sufficient Skills in Docker, bash and GitHub are highly recommended as well.

Software Requirements:

Generally, the installation of the latest Anaconda 3 distribution will suffice as it contains NumPy, Pandas, Sklearn and lots of other libraries as well. However, some other packages like Xgboostand Vowpal Wabbit are also made use of.

Moving onto the Demo Assignments:

Every week in a new run of the course announcement of full assignments is made (October 1, 2018). While that takes place, an applicant can practice with the demo versions. Solutions to both demos as well as full versions are to be discussed in the upcoming run of the course: